Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management - Penghui Xu; Xiaobo Wang; Zhichao Li (Energy Informatics)
Abstract Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development.